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Arctic sea-ice change tied to its mean state through thermodynamic processes

Author

Listed:
  • François Massonnet

    (Université catholique de Louvain (UCL)
    Barcelona Supercomputing Center)

  • Martin Vancoppenolle

    (Sorbonne Universités (UPMC Paris 6), LOCEAN-IPSL, CNRS/IRD/MNHN)

  • Hugues Goosse

    (Université catholique de Louvain (UCL))

  • David Docquier

    (Université catholique de Louvain (UCL))

  • Thierry Fichefet

    (Université catholique de Louvain (UCL))

  • Edward Blanchard-Wrigglesworth

    (University of Washington)

Abstract

One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state-of-the-art climate models consistently reproduce the sign of these changes, they largely disagree on their magnitude1–4, the reasons for which remain contentious3,5–7. As such, consensual methods to reduce uncertainty in projections are lacking7. Here, using the CMIP5 ensemble, we propose a process-oriented approach to revisit this issue. We show that intermodel differences in sea-ice loss and, more generally, in simulated sea-ice variability, can be traced to differences in the simulation of seasonal growth and melt. The way these processes are simulated is relatively independent of the complexity of the sea-ice model used, but rather a strong function of the background thickness. The larger role played by thermodynamic processes as sea ice thins8,9 further suggests that the recent10 and projected11 reductions in sea-ice thickness induce a transition of the Arctic towards a state with enhanced volume seasonality but reduced interannual volume variability and persistence, before summer ice-free conditions eventually occur. These results prompt modelling groups to focus their priorities on the reduction of sea-ice thickness biases.

Suggested Citation

  • François Massonnet & Martin Vancoppenolle & Hugues Goosse & David Docquier & Thierry Fichefet & Edward Blanchard-Wrigglesworth, 2018. "Arctic sea-ice change tied to its mean state through thermodynamic processes," Nature Climate Change, Nature, vol. 8(7), pages 599-603, July.
  • Handle: RePEc:nat:natcli:v:8:y:2018:i:7:d:10.1038_s41558-018-0204-z
    DOI: 10.1038/s41558-018-0204-z
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    Cited by:

    1. Xueke Li & Scott R. Stephenson & Amanda H. Lynch & Michael A. Goldstein & David A. Bailey & Siri Veland, 2021. "Arctic shipping guidance from the CMIP6 ensemble on operational and infrastructural timescales," Climatic Change, Springer, vol. 167(1), pages 1-19, July.
    2. William Gregory & Ronald MacEachern & So Takao & Isobel R. Lawrence & Carmen Nab & Marc Peter Deisenroth & Michel Tsamados, 2024. "Scalable interpolation of satellite altimetry data with probabilistic machine learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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